SOTAVerified

Feature Engineering

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

Papers

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Showing 1301–1310 of 1706 papers

TitleStatusHype
A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging—0
A Feature Induction Algorithm with Application to Named Entity Disambiguation—0
Affect inTweets: A Transfer Learning Approach—0
A Flexible and Easy-to-use Semantic Role Labeling Framework for Different Languages—0
A Food Recommender System in Academic Environments Based on Machine Learning Models—0
A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading—0
A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering—0
A Gated Recurrent Unit Approach to Bitcoin Price Prediction—0
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis—0
A generalized financial time series forecasting model based on automatic feature engineering using genetic algorithms and support vector machine—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified